%matplotlib inline

import glob
import matplotlib
from matplotlib import pyplot
import numpy
import os
import pandas
import seaborn
import sys

pandas.options.display.max_columns = None
pandas.options.display.max_rows = None

matplotlib.rcParams['figure.dpi'] = 200
matplotlib.rcParams['agg.path.chunksize'] = 10000
print(f'Package versions:\nPython {sys.version}, matplotlib {matplotlib.__version__}, '
      f'numpy {numpy.__version__}, pandas {pandas.__version__}, '
      f'seaborn {seaborn.__version__}')

Package versions:
Python 3.7.12 | packaged by conda-forge | (default, Oct 26 2021, 06:08:53) 
[GCC 9.4.0], matplotlib 3.4.2, numpy 1.21.0, pandas 1.1.5, seaborn 0.11.1

drop_dups = pandas.read_csv('./read_dataframe.csv')
print(drop_dups.shape)
drop_dups.head()

(11774142, 12)

	well_id	r1	r2	seq_corr	edit_type	umi	bc_tracer	het_spacer_offset	bc1_corr	bc2_corr	condition	read_count
0	A1	NCCATTCTCATCTAAACCCCCTGAAGCTTCACCAGCGCAATTATTC...	NAGACGTGTGCTCTTCCGACGTCTACACTCGNNNNNNNCCACCGTN...	UNKNOWN	UNKNOWN	UNKNOWN	UNKNOWN	UNKNOWN	UNKNOWN	UNKNOWN	UNKNOWN	1
1	A1	GCCATTCTCATCCAAACCCCCTGAAGCTTCACCGGCGCAGTCATTC...	NAGACGTGTGCTCTTCCGAGTGCGTGAAATACCGTCAACACACGTG...	GCCATTCTCATCCAAACCCCCTGAAGCTTCACCGGCGCAGTCATTC...	WT	GTGCGTGAAA	UNKNOWN	0	CTCAATGA	TACCGTCA	d5_low_silent_r1	1
2	A1	GCCATTCTCATCCAAACCCCCTGAAGCTTCACCGGCGCAGTCATTC...	NAGACGTGTGCTCTTCCATTATGCGGGTCCGTCGCGATCCACGTGC...	GCCATTCTCATCCAAACCCCCTGAAGCTTCACCGGCGCAGTCATTC...	WT	TATGCGGGTC	UNKNOWN	0	UNKNOWN	UNKNOWN	UNKNOWN	1
3	A1	GCCATTCTCATCCAAACCCCCTGAAGCTTCACCGGCGCAGTCATTC...	NAGACGTGTGCTCTTCCGATATGTGCGCTTAAGGTAAATCCACTTG...	GCCATTCTCATCCAAACCCCCTGAAGCTTCACCGGCGCAGTCATTC...	WT	TATGTGCGCT	UNKNOWN	0	CTCAATGA	TAAGGTAA	d5_low_silent_r1	1
4	A1	GCCATTCTCATCCAAACCCCCTGAAGCTTCACCGGCGCAGTCATTC...	CAGACGTGTGCCCTTTCGATTGACTGGTATACGGTCAAACAACGTG...	GCCATTCTCATCCAAACCCCCTGAAGCTTCACCGGCGCAGTCATTC...	WT	TTGACTGGTA	UNKNOWN	0	UNKNOWN	TACCGTCA	UNKNOWN	1

Make barplot

Make plots for the read 1 matches

all_stats = drop_dups.groupby('edit_type')['read_count'].sum()
all_stats['TOTAL'] = all_stats.sum()
all_stats = all_stats.reset_index().sort_values(by='read_count', ascending=False)
all_stats

	edit_type	read_count
10	TOTAL	11883915
9	WT	6889727
5	SILENT	2473967
1	LHON_BY1	1279990
8	UNKNOWN	759119
0	LHON	127794
3	LHON_BY3	100533
2	LHON_BY2	78902
6	SILENT_BY1	78739
4	LHON_BY4	67603
7	SILENT_BY2	27541

#plot read 1 matches
fig, axes = pyplot.subplots(nrows=1, ncols=1, figsize=(6,4))
seaborn.barplot(data=all_stats, x='edit_type', y='read_count', ax=axes)
axes.set_xticklabels(axes.get_xticklabels(), rotation=45, ha='right', fontsize=10)
axes.set_xlabel('')
axes.set_ylabel('Read count')
fig.tight_layout()

fig, axes = pyplot.subplots(nrows=1, ncols=1, figsize=(4,4))
axes.bar(x=0, height=all_stats.loc[all_stats['edit_type'] == 'TOTAL', 'read_count'], color='C0')

stacked_indices = ~all_stats['edit_type'].isin(['UNKNOWN', 'TOTAL'])
bottom=0
for r1_name, val in all_stats.loc[stacked_indices].to_numpy():
    axes.bar(x=1, height=val, bottom=bottom, label=r1_name)
    bottom += val

axes.bar(x=2, height=all_stats.loc[all_stats['edit_type'] == 'UNKNOWN', 'read_count'], color='C0')

axes.set_xticks(numpy.arange(3))
axes.set_xticklabels(['Total reads', 'Expected matches', 'Unmatched'], ha='right', rotation=35)
axes.set_ylabel('Read count')
axes.legend(bbox_to_anchor=[1,1])
fig.tight_layout()

Make plots for the read2 matches

all_stats = drop_dups.groupby('condition')['read_count'].sum()
all_stats['TOTAL'] = all_stats.sum()
order = ['TOTAL', 'UNKNOWN'] + sorted(all_stats.loc[~all_stats.index.isin(['TOTAL', 'UNKNOWN'])].index.values)
all_stats = all_stats.loc[order].rename('read_count').reset_index()
all_stats

	condition	read_count
0	TOTAL	11883915
1	UNKNOWN	4999055
2	d0_lhon_r1	143634
3	d0_lhon_r2	146308
4	d0_lhon_r3	196591
5	d0_silent_r1	260238
6	d0_silent_r2	220869
7	d0_silent_r3	126572
8	d5_high_lhon_r1	159514
9	d5_high_lhon_r2	191347
10	d5_high_lhon_r3	176685
11	d5_high_silent_r1	256625
12	d5_high_silent_r2	320269
13	d5_high_silent_r3	305760
14	d5_low_lhon_r1	216357
15	d5_low_lhon_r3	147897
16	d5_low_silent_r1	306902
17	d5_low_silent_r2	205732
18	d5_low_silent_r3	219215
19	d5_med_lhon_r1	478868
20	d5_med_lhon_r2	506895
21	d5_med_lhon_r3	482356
22	d5_unsort_lhon_r1	258787
23	d5_unsort_lhon_r2	360709
24	d5_unsort_lhon_r3	291066
25	d5_unsort_silent_r1	275515
26	d5_unsort_silent_r2	358604
27	d5_unsort_silent_r3	271545

#plot read 2 matches
fig, axes = pyplot.subplots(nrows=1, ncols=1, figsize=(8,3))
seaborn.barplot(data=all_stats, x='condition', y='read_count', ax=axes)
axes.set_xticklabels(axes.get_xticklabels(), rotation=45, ha='right', fontsize=10)
axes.set_xlabel('')
axes.set_ylabel('Read count')
fig.tight_layout()

#plot read 2 matches
fig, axes = pyplot.subplots(nrows=1, ncols=1, figsize=(8,3))
seaborn.barplot(data=all_stats, x='condition', y='read_count', ax=axes)
axes.set_yscale('log')
axes.set_xticklabels(axes.get_xticklabels(), rotation=45, ha='right', fontsize=10)
axes.set_xlabel('')
axes.set_ylabel('Read count')
fig.tight_layout()

fig, axes = pyplot.subplots(nrows=1, ncols=1, figsize=(8,4))
axes.bar(x=0, height=all_stats.loc[all_stats['condition'] == 'TOTAL', 'read_count'], color='C0')

stacked_indices = ~all_stats['condition'].isin(['UNKNOWN', 'TOTAL'])
bottom=0
for r1_name, val in all_stats.loc[stacked_indices].to_numpy():
    axes.bar(x=1, height=val, bottom=bottom, label=r1_name)
    bottom += val

axes.bar(x=2, height=all_stats.loc[all_stats['condition'] == 'UNKNOWN', 'read_count'], color='C0')

axes.bar(x=3, height=numpy.sum(drop_dups['bc2_corr'] != 'UNKNOWN'), color='C0')
axes.bar(x=4, height=numpy.sum(drop_dups['bc2_corr'] == 'UNKNOWN'), color='C0')

axes.set_xticks(numpy.arange(5))
axes.set_xticklabels(['Total reads', 'Matched bc1', 'Unmatched bc1', 
                      'Matched bc2', 'Unmatched bc2'], ha='right', rotation=35)
axes.set_ylabel('Read count')
axes.legend(bbox_to_anchor=[1,1], fontsize=6)
fig.tight_layout()

valid_cells = pandas.read_csv('./heteroplasmy_dataframe.csv')
print(valid_cells.shape)
valid_cells.head()

(9978, 13)

	cell_id	condition	umi_count	LHON	LHON/SILENT	SILENT	WT	umi_count_for_filtering	umi_count_rank	LHON_het	SILENT_het	SILENT_ONLY_het	total_het
0	A1-CTCAATGA-TAAGGTAA	d5_low_silent_r1	34	0	1	2	31	34	4902	0.029412	0.088235	0.058824	0.088235
1	A1-ACACAGAA-GATACGCA	d5_unsort_lhon_r3	37	0	3	1	33	37	4429	0.081081	0.108108	0.027027	0.108108
2	A1-AACAACCA-CGTTAGAT	d5_unsort_lhon_r1	30	0	4	8	18	30	6043	0.133333	0.400000	0.266667	0.400000
3	A1-ACGTATCA-GCATGCCG	d5_unsort_silent_r2	25	0	3	7	15	25	8087	0.120000	0.400000	0.280000	0.400000
4	A1-CCGTGAGA-CCAGGCTT	d5_low_lhon_r1	24	0	2	1	21	24	8095	0.083333	0.125000	0.041667	0.125000

fig, axes = pyplot.subplots(nrows=1, ncols=1, figsize=(7,4))
xorder = sorted(valid_cells['condition'].unique())
seaborn.countplot(data=valid_cells, x='condition', order=xorder, ax=axes)
axes.set_xticklabels(axes.get_xticklabels(), ha='right', rotation=45)
fig.tight_layout()

bulk_estimates = pandas.read_csv('../Bulk_LHON_timeseries/mut_counts.csv')
bulk_estimates['TOTAL_het'] = bulk_estimates[['LHON', 'LHON_BY1', 'LHON_BY2', 'LHON_BY3', 'LHON_BY4',
                                              'SILENT', 'SILENT_BY1', 'SILENT_BY2']].sum(axis=1)/bulk_estimates['TOTAL']
print(bulk_estimates.shape)
bulk_estimates

(72, 19)

	sample_id	LHON	LHON_BY1	LHON_BY2	LHON_BY3	LHON_BY4	MUT	SILENT	SILENT_BY1	SILENT_BY2	UNKNOWN	WT	TOTAL	LHON_het	SILENT_het	MUT_het	sample_id.1	sci-lite_sample	TOTAL_het
0	A1	111	190	11	12	8	0	534	10	6	271	24419	25301	0.013122	0.021738	0.000000	1NT-unsorted_d0-0_d0	none	0.034860
1	A10	6390	101575	4285	4758	4678	0	101644	3572	2396	11507	514283	743581	0.163649	0.144721	0.000000	1LHON-unsorted_d5-0_d0	d5_unsort_lhon_r1	0.308370
2	A11	1888	19880	4817	1039	1054	0	43569	679	695	11625	201180	274801	0.104359	0.163547	0.000000	2LHON-unsorted_d5-0_d0	d5_unsort_lhon_r2	0.267907
3	A12	80	947	41	40	41	0	1142	26	28	206	5101	7446	0.154311	0.160623	0.000000	3LHON-unsorted_d5-0_d0	d5_unsort_lhon_r3	0.314934
4	A2	11	38	3	3	4	0	153	5	2	39	1158	1377	0.042847	0.116195	0.000000	2NT-unsorted_d0-0_d0	none	0.159041
5	A3	57	82	3	2	6	0	310	5	2	272	24377	24844	0.006038	0.012760	0.000000	3NT-unsorted_d0-0_d0	none	0.018797
6	A4	67	154	7	12	10	0	520	16	4	121	6995	7785	0.032113	0.069364	0.000000	1LHON-unsorted_d0-0_d0	d0_lhon_r1	0.101477
7	A5	131	216	9	16	5	0	1481	19	6	263	22861	24744	0.015236	0.060863	0.000000	2LHON-unsorted_d0-0_d0	d0_lhon_r2	0.076099
8	A6	157	281	1	19	10	0	3371	32	8	525	43184	47063	0.009944	0.072477	0.000000	3LHON-unsorted_d0-0_d0	d0_lhon_r3	0.082421
9	A7	62	140	12	12	5	0	416	8	6	101	3466	4127	0.055973	0.104192	0.000000	1Silent-unsorted_d0-0_d0	d0_silent_r1	0.160165
10	A8	409	243	5	18	4	0	2707	16	7	1281	121785	125194	0.005424	0.021806	0.000000	2Silent-unsorted_d0-0_d0	d0_silent_r2	0.027230
11	A9	30	76	0	4	4	0	340	5	1	101	4258	4718	0.024163	0.073336	0.000000	3Silent-unsorted_d0-0_d0	d0_silent_r3	0.097499
12	B1	341	483	17	40	11	0	21053	45	21	1569	115720	137731	0.006476	0.153335	0.000000	1Silent-unsorted_d5-0_d0	d5_unsort_silent_r1	0.159812
13	B10	81	269	17	31	12	0	916	22	3	93	2012	3363	0.121915	0.279810	0.000000	1LHON-low_d5-0_d0	d5_low_lhon_r1	0.401725
14	B11	1386	10825	780	1285	609	0	17871	730	277	4754	300132	333895	0.044580	0.056539	0.000000	2LHON-low_d5-0_d0	d5_low_lhon_r2	0.101119
15	B12	1722	11051	628	821	496	0	21635	687	331	5070	351290	388661	0.037868	0.058285	0.000000	3LHON-low_d5-0_d0	d5_low_lhon_r3	0.096153
16	B2	336	488	192	28	14	0	20311	35	17	1637	119882	141303	0.007487	0.144109	0.000000	2Silent-unsorted_d5-0_d0	d5_unsort_silent_r2	0.151596
17	B3	85	189	21	22	10	0	2409	27	4	202	11294	14061	0.023256	0.173530	0.000000	3Silent-unsorted_d5-0_d0	d5_unsort_silent_r3	0.196785
18	B4	6286	165698	15446	19788	11901	0	83167	12670	2370	13253	49682	367008	0.597041	0.267588	0.000000	1LHON-high_d5-0_d0	d5_high_lhon_r1	0.864630
19	B5	6114	152016	14564	18739	10748	0	79486	12262	2158	12658	60647	356734	0.566756	0.263238	0.000000	2LHON-high_d5-0_d0	d5_high_lhon_r2	0.829994
20	B6	5701	140764	16662	25153	12162	0	69995	14884	1997	14582	78981	366299	0.547209	0.237172	0.000000	3LHON-high_d5-0_d0	d5_high_lhon_r3	0.784381
21	B7	720	1666	67	173	43	0	244398	194	155	6326	179863	427279	0.006247	0.572804	0.000000	1Silent-high_d5-0_d0	d5_high_silent_r1	0.579050
22	B8	575	1042	38	97	30	0	181182	120	126	4938	195178	378388	0.004709	0.479476	0.000000	2Silent-high_d5-0_d0	d5_high_silent_r2	0.484186
23	B9	466	894	28	74	23	0	190170	131	144	4854	178020	369950	0.004014	0.514786	0.000000	3Silent-high_d5-0_d0	d5_high_silent_r3	0.518800
24	C1	573	326	4	22	5	0	10093	21	7	3862	366936	377987	0.002460	0.026776	0.000000	1Silent-low_d5-0_d0	d5_low_silent_r1	0.029236
25	C10	393	272	5	8	11	0	51445	24	25	3592	305541	357724	0.001926	0.143949	0.000000	1Silent-unsorted_d10-GLU_d4	none	0.145875
26	C11	540	296	4	20	2	0	52679	30	32	3802	318857	372460	0.002314	0.141602	0.000000	2Silent-unsorted_d10-GLU_d4	none	0.143916
27	C12	350	234	7	10	11	0	28112	23	17	2041	180424	209188	0.002926	0.134578	0.000000	3Silent-unsorted_d10-GLU_d4	none	0.137503
28	C2	114	110	5	5	3	0	2125	6	4	754	67089	69461	0.003412	0.030737	0.000000	2Silent-low_d5-0_d0	d5_low_silent_r2	0.034149
29	C3	491	237	3	4	6	0	10957	18	9	4272	406615	418340	0.001771	0.026256	0.000000	3Silent-low_d5-0_d0	d5_low_silent_r3	0.028027
30	C4	4779	54458	1928	3235	2288	0	90474	2940	1536	6828	112624	274262	0.243154	0.346202	0.000000	1LHON-med_d5-0_d0	d5_med_lhon_r1	0.589356
31	C5	5587	70711	2338	4181	2982	0	123057	3473	2171	7680	140079	354579	0.241974	0.362968	0.000000	2LHON-med_d5-0_d0	d5_med_lhon_r2	0.604943
32	C6	5235	75393	2891	4888	3495	0	121636	4212	2124	8233	159972	379846	0.241945	0.336905	0.000000	3LHON-med_d5-0_d0	d5_med_lhon_r3	0.578850
33	C7	2017	16850	385	586	632	0	31892	532	628	3684	240855	294377	0.069537	0.112278	0.000000	1LHON-unsorted_d10-GLU_d4	none	0.181814
34	C8	2225	23496	620	715	848	0	42396	772	793	4520	286123	357988	0.077947	0.122800	0.000000	2LHON-unsorted_d10-GLU_d4	none	0.200747
35	C9	2163	23226	575	713	830	0	43558	835	782	4021	255424	328106	0.083836	0.137684	0.000000	3LHON-unsorted_d10-GLU_d4	none	0.221520
36	D1	553	4062	98	155	140	0	8904	152	142	733	51495	65701	0.076224	0.139998	0.000000	1LHON-unsorted_d10-GAL_d4	none	0.216222
37	D10	541	613	11	35	13	0	182086	85	77	4615	237922	421383	0.002879	0.432500	0.000000	2Silent-high_d10-GLU_d4	none	0.435378
38	D11	609	798	10	46	15	0	187402	89	121	4762	203848	392938	0.003761	0.477460	0.000000	3Silent-high_d10-GLU_d4	none	0.481221
39	D12	5534	69935	4121	5387	3524	0	83379	5566	1513	7766	236222	415181	0.213162	0.217876	0.000000	1LHON-high_d10-GAL_d4	none	0.431039
40	D2	972	8272	166	300	292	0	17881	258	359	1595	110665	139165	0.071872	0.132921	0.000000	2LHON-unsorted_d10-GAL_d4	none	0.204793
41	D3	1815	14652	259	497	504	0	37305	460	635	3496	253123	309250	0.057323	0.124171	0.000000	3LHON-unsorted_d10-GAL_d4	none	0.181494
42	D4	1000	739	13	68	19	0	82856	65	55	4430	365728	450543	0.004082	0.184169	0.000000	1Silent-unsorted_d10-GAL_d4	none	0.188251
43	D5	687	615	17	42	16	0	46946	35	54	3320	274895	323307	0.004259	0.145481	0.000000	2Silent-unsorted_d10-GAL_d4	none	0.149740
44	D6	722	780	28	61	31	0	43519	65	34	2933	251424	296664	0.005467	0.147028	0.000000	3Silent-unsorted_d10-GAL_d4	none	0.152496
45	D7	8789	136233	9618	14860	8604	0	107148	10335	2667	14039	180924	479178	0.371687	0.250742	0.000000	1LHON-high_d10-GLU_d4	none	0.622428
46	D8	6893	101981	6578	10742	6472	0	90273	7423	2095	11067	202062	434519	0.305317	0.229659	0.000000	2LHON-high_d10-GLU_d4	none	0.534975
47	D9	466	747	18	56	15	0	236025	110	120	5070	199278	436835	0.002981	0.540833	0.000000	1Silent-high_d10-GLU_d4	none	0.543814
48	E1	4709	51531	2521	3898	2547	0	74597	4009	1369	6828	261994	407175	0.160142	0.196414	0.000000	2LHON-high_d10-GAL_d4	none	0.356557
49	E10	2436	22975	596	1147	842	0	55377	1246	823	2918	86217	171659	0.163091	0.334652	0.000000	3LHON-med_d10-GAL_d4	none	0.497743
50	E11	1609	12848	667	778	563	0	27778	726	401	4970	454386	499756	0.032946	0.057838	0.000000	1LHON-low_d10-GLU_d4	none	0.090784
51	E12	1756	10807	489	628	422	0	25713	638	308	4632	460323	501084	0.028143	0.053203	0.000000	3LHON-low_d10-GLU_d4	none	0.081346
52	E2	284	414	2	31	7	0	174186	47	87	3510	156357	331415	0.002227	0.525987	0.000000	1Silent-high_d10-GAL_d4	none	0.528214
53	E3	304	370	8	13	7	0	181750	54	97	4182	227604	410207	0.001711	0.443437	0.000000	2Silent-high_d10-GAL_d4	none	0.445148
54	E4	941	1901	71	172	44	0	57309	180	108	3140	77485	138211	0.022639	0.416732	0.000000	3Silent-high_d10-GAL_d4	none	0.439372
55	E5	5918	53563	1293	2296	1899	0	136771	2919	2035	7237	201009	407703	0.159354	0.347618	0.000000	1LHON-med_d10-GLU_d4	none	0.506972
56	E6	6120	64976	1776	2889	2208	0	153865	3459	2185	8058	186054	423532	0.184092	0.376616	0.000000	2LHON-med_d10-GLU_d4	none	0.560709
57	E7	6890	69399	2046	3278	2503	0	151583	4002	2302	8694	209936	451939	0.186122	0.349355	0.000000	3LHON-med_d10-GLU_d4	none	0.535477
58	E8	510	4945	110	219	139	0	12103	265	170	703	17250	35711	0.165859	0.351096	0.000000	1LHON-med_d10-GAL_d4	none	0.516956
59	E9	1578	15134	344	634	544	0	37366	668	555	1852	45710	102533	0.177835	0.376357	0.000000	2LHON-med_d10-GAL_d4	none	0.554192
60	F1	1148	618	21	40	20	0	7469	57	25	2955	230307	239705	0.007705	0.031501	0.000000	1Silent-low_d10-GLU_d4	none	0.039207
61	F10	0	0	0	0	0	59541	0	0	0	3234	275163	334704	0.000000	0.000000	0.177892	None-unsorted_d0-None_d0	none	0.000000
62	F11	0	0	0	0	0	34403	0	0	0	4465	277537	311940	0.000000	0.000000	0.110287	None-unsorted_d0-None_d0	none	0.000000
63	F12	0	0	0	0	0	70741	0	0	0	3383	119615	190356	0.000000	0.000000	0.371625	None-unsorted_d0-None_d0	none	0.000000
64	F2	1021	343	11	31	6	0	10511	36	20	4148	369187	381166	0.003704	0.027723	0.000000	2Silent-low_d10-GLU_d4	none	0.031427
65	F3	655	206	4	16	8	0	7542	15	8	3414	332111	340565	0.002610	0.022213	0.000000	3Silent-low_d10-GLU_d4	none	0.024823
66	F4	1004	2439	71	140	82	0	8947	122	88	2543	194648	207541	0.018001	0.044121	0.000000	1LHON-low_d10-GAL_d4	none	0.062123
67	F5	1030	2796	88	97	79	0	10639	174	110	2878	233426	248439	0.016463	0.043967	0.000000	3LHON-low_d10-GAL_d4	none	0.060429
68	F6	807	517	12	53	13	0	6161	48	17	2413	192850	200478	0.006993	0.031056	0.000000	1Silent-low_d10-GAL_d4	none	0.038049
69	F7	935	459	14	28	12	0	8103	34	24	2866	242579	252188	0.005742	0.032361	0.000000	2Silent-low_d10-GAL_d4	none	0.038103
70	F8	520	256	5	22	8	0	4375	25	13	1676	154140	159364	0.005089	0.027691	0.000000	3Silent-low_d10-GAL_d4	none	0.032780
71	F9	0	0	0	0	0	2825	0	0	0	2574	8340	11165	0.000000	0.000000	0.253023	None-unsorted_d0-None_d0	none	0.000000

fig, axes = pyplot.subplots(nrows=1, ncols=1, figsize=(10,4))
xorder = ['d0_lhon_r1', 'd0_lhon_r2', 'd0_lhon_r3',
          'd5_low_lhon_r1', 'd5_low_lhon_r3',
          'd5_med_lhon_r1', 'd5_med_lhon_r2', 'd5_med_lhon_r3',
          'd5_high_lhon_r1', 'd5_high_lhon_r2', 'd5_high_lhon_r3',
          'd5_unsort_lhon_r1', 'd5_unsort_lhon_r2', 'd5_unsort_lhon_r3',
          'd0_silent_r1', 'd0_silent_r2', 'd0_silent_r3',
          'd5_low_silent_r1', 'd5_low_silent_r2', 'd5_low_silent_r3',
          'd5_high_silent_r1', 'd5_high_silent_r2', 'd5_high_silent_r3',
          'd5_unsort_silent_r1', 'd5_unsort_silent_r2', 'd5_unsort_silent_r3']
seaborn.boxplot(data=valid_cells, x='condition', y='total_het', ax=axes, order=xorder, showfliers=False)
seaborn.stripplot(data=valid_cells, x='condition', y='total_het', dodge=True, s=1, order=xorder, ax=axes)
axes.plot(numpy.arange(len(xorder)), valid_cells.groupby('condition')['total_het'].mean()[xorder],
          color='k', marker='^', linestyle='', zorder=100, label='mean het estimate')

for patch in axes.artists:
    r, g, b, a = patch.get_facecolor()
    patch.set_facecolor((r, g, b, 0))
    patch.set_edgecolor((r, g, b, 1))
    
axes.set_xticklabels(axes.get_xticklabels(), ha='right', rotation=45)
axes.set_ylim((-0.03, 1.03))
axes.set_yticks(numpy.arange(0, 1.1, 0.1))

xpos = numpy.arange(len(xorder))
bulk_hets = []
for sname in xorder:
    bulk_hets.append(bulk_estimates.loc[bulk_estimates['sci-lite_sample'] == sname, 'TOTAL_het'].to_numpy()[0])
axes.plot(xpos, bulk_hets, color='k', marker='*', linestyle='', zorder=100, label='bulk het estimate')
axes.legend(bbox_to_anchor=[1,1])

fig.tight_layout()

fig, axes = pyplot.subplots(nrows=1, ncols=1, figsize=(6,4))
xorder = ['d0_lhon_r1', 'd0_lhon_r2', 'd0_lhon_r3',
          'd5_low_lhon_r1', 'd5_low_lhon_r3',
          'd5_med_lhon_r1', 'd5_med_lhon_r2', 'd5_med_lhon_r3',
          'd5_high_lhon_r1', 'd5_high_lhon_r2', 'd5_high_lhon_r3',
          'd5_unsort_lhon_r1', 'd5_unsort_lhon_r2', 'd5_unsort_lhon_r3']
seaborn.boxplot(data=valid_cells, x='condition', y='LHON_het', ax=axes, order=xorder, showfliers=False)
seaborn.stripplot(data=valid_cells, x='condition', y='LHON_het', dodge=True, s=1, order=xorder, ax=axes)
axes.plot(numpy.arange(len(xorder)), valid_cells.groupby('condition')['LHON_het'].mean()[xorder],
          color='k', marker='^', linestyle='', zorder=100, label='mean het estimate')

for patch in axes.artists:
    r, g, b, a = patch.get_facecolor()
    patch.set_facecolor((r, g, b, 0))
    patch.set_edgecolor((r, g, b, 1))
    
axes.set_xticklabels(axes.get_xticklabels(), ha='right', rotation=45)
axes.set_ylim((-0.03, 1.03))
axes.set_yticks(numpy.arange(0, 1.1, 0.1))

xpos = numpy.arange(len(xorder))
bulk_hets = []
for sname in xorder:
    bulk_hets.append(bulk_estimates.loc[bulk_estimates['sci-lite_sample'] == sname, 'LHON_het'].to_numpy()[0])
axes.plot(xpos, bulk_hets, color='k', marker='*', linestyle='', zorder=100, label='bulk het estimate')
axes.legend(bbox_to_anchor=[1,1])

fig.tight_layout()

fig, axes = pyplot.subplots(nrows=1, ncols=1, figsize=(6,4))
xorder = ['d0_lhon_r1', 'd0_lhon_r2', 'd0_lhon_r3',
          'd5_low_lhon_r1', 'd5_low_lhon_r3',
          'd5_med_lhon_r1', 'd5_med_lhon_r2', 'd5_med_lhon_r3',
          'd5_high_lhon_r1', 'd5_high_lhon_r2', 'd5_high_lhon_r3',
          'd5_unsort_lhon_r1', 'd5_unsort_lhon_r2', 'd5_unsort_lhon_r3']
seaborn.boxplot(data=valid_cells, x='condition', y='SILENT_ONLY_het', ax=axes, order=xorder, showfliers=False)
seaborn.stripplot(data=valid_cells, x='condition', y='SILENT_ONLY_het', dodge=True, s=1, order=xorder, ax=axes)
axes.plot(numpy.arange(len(xorder)), valid_cells.groupby('condition')['SILENT_ONLY_het'].mean()[xorder],
          color='k', marker='^', linestyle='', zorder=100, label='mean het estimate')

for patch in axes.artists:
    r, g, b, a = patch.get_facecolor()
    patch.set_facecolor((r, g, b, 0))
    patch.set_edgecolor((r, g, b, 1))
    
axes.set_xticklabels(axes.get_xticklabels(), ha='right', rotation=45)
axes.set_ylim((-0.03, 1.03))
axes.set_yticks(numpy.arange(0, 1.1, 0.1))

xpos = numpy.arange(len(xorder))
bulk_hets = []
for sname in xorder:
    bulk_hets.append(bulk_estimates.loc[bulk_estimates['sci-lite_sample'] == sname, 'SILENT_het'].to_numpy()[0])
axes.plot(xpos, bulk_hets, color='k', marker='*', linestyle='', zorder=100, label='bulk het estimate')
axes.legend(bbox_to_anchor=[1,1])

fig.tight_layout()

fig, axes = pyplot.subplots(nrows=1, ncols=1, figsize=(6,4))
xorder = ['d0_silent_r1', 'd0_silent_r2', 'd0_silent_r3',
          'd5_low_silent_r1', 'd5_low_silent_r2', 'd5_low_silent_r3',
          'd5_high_silent_r1', 'd5_high_silent_r2', 'd5_high_silent_r3',
          'd5_unsort_silent_r1', 'd5_unsort_silent_r2', 'd5_unsort_silent_r3']
seaborn.boxplot(data=valid_cells, x='condition', y='SILENT_ONLY_het', ax=axes, order=xorder, showfliers=False)
seaborn.stripplot(data=valid_cells, x='condition', y='SILENT_ONLY_het', dodge=True, s=1, order=xorder, ax=axes)
axes.plot(numpy.arange(len(xorder)), valid_cells.groupby('condition')['SILENT_ONLY_het'].mean()[xorder],
          color='k', marker='^', linestyle='', zorder=100, label='mean het estimate')

for patch in axes.artists:
    r, g, b, a = patch.get_facecolor()
    patch.set_facecolor((r, g, b, 0))
    patch.set_edgecolor((r, g, b, 1))
    
axes.set_xticklabels(axes.get_xticklabels(), ha='right', rotation=45)
axes.set_ylim((-0.03, 1.03))
axes.set_yticks(numpy.arange(0, 1.1, 0.1))

xpos = numpy.arange(len(xorder))
bulk_hets = []
for sname in xorder:
    bulk_hets.append(bulk_estimates.loc[bulk_estimates['sci-lite_sample'] == sname, 'SILENT_het'].to_numpy()[0])
axes.plot(xpos, bulk_hets, color='k', marker='*', linestyle='', zorder=100, label='bulk het estimate')
axes.legend(bbox_to_anchor=[1,1])

fig.tight_layout()

fig, axes = pyplot.subplots(nrows=1, ncols=1, figsize=(6,4))
xorder = ['d0_silent_r1', 'd0_silent_r2', 'd0_silent_r3',
          'd5_low_silent_r1', 'd5_low_silent_r2', 'd5_low_silent_r3',
          'd5_high_silent_r1', 'd5_high_silent_r2', 'd5_high_silent_r3',
          'd5_unsort_silent_r1', 'd5_unsort_silent_r2', 'd5_unsort_silent_r3']
seaborn.boxplot(data=valid_cells, x='condition', y='LHON_het', ax=axes, order=xorder, showfliers=False)
seaborn.stripplot(data=valid_cells, x='condition', y='LHON_het', dodge=True, s=1, order=xorder, ax=axes)
axes.plot(numpy.arange(len(xorder)), valid_cells.groupby('condition')['LHON_het'].mean()[xorder],
          color='k', marker='^', linestyle='', zorder=100, label='mean het estimate')

for patch in axes.artists:
    r, g, b, a = patch.get_facecolor()
    patch.set_facecolor((r, g, b, 0))
    patch.set_edgecolor((r, g, b, 1))
    
axes.set_xticklabels(axes.get_xticklabels(), ha='right', rotation=45)
axes.set_ylim((-0.03, 1.03))
axes.set_yticks(numpy.arange(0, 1.1, 0.1))

xpos = numpy.arange(len(xorder))
bulk_hets = []
for sname in xorder:
    bulk_hets.append(bulk_estimates.loc[bulk_estimates['sci-lite_sample'] == sname, 'LHON_het'].to_numpy()[0])
axes.plot(xpos, bulk_hets, color='k', marker='*', linestyle='', zorder=100, label='bulk het estimate')
axes.legend(bbox_to_anchor=[1,1])

fig.tight_layout()

fig, axes = pyplot.subplots(nrows=1, ncols=1, figsize=(10,4))
xorder = ['d0_lhon_r1', 'd0_lhon_r2', 'd0_lhon_r3',
          'd5_low_lhon_r1', 'd5_low_lhon_r3',
          'd5_med_lhon_r1', 'd5_med_lhon_r2', 'd5_med_lhon_r3',
          'd5_high_lhon_r1', 'd5_high_lhon_r2', 'd5_high_lhon_r3',
          'd5_unsort_lhon_r1', 'd5_unsort_lhon_r2', 'd5_unsort_lhon_r3',
          'd0_silent_r1', 'd0_silent_r2', 'd0_silent_r3',
          'd5_low_silent_r1', 'd5_low_silent_r2', 'd5_low_silent_r3',
          'd5_high_silent_r1', 'd5_high_silent_r2', 'd5_high_silent_r3',
          'd5_unsort_silent_r1', 'd5_unsort_silent_r2', 'd5_unsort_silent_r3']
seaborn.boxplot(data=valid_cells, x='condition', y='LHON_het', ax=axes, order=xorder, showfliers=False)
seaborn.stripplot(data=valid_cells, x='condition', y='LHON_het', dodge=True, s=1, order=xorder, ax=axes)
axes.plot(numpy.arange(len(xorder)), valid_cells.groupby('condition')['LHON_het'].mean()[xorder],
          color='k', marker='^', linestyle='', zorder=100, label='mean het estimate')

for patch in axes.artists:
    r, g, b, a = patch.get_facecolor()
    patch.set_facecolor((r, g, b, 0))
    patch.set_edgecolor((r, g, b, 1))
    
axes.set_xticklabels(axes.get_xticklabels(), ha='right', rotation=45)
axes.set_ylim((-0.03, 1.03))
axes.set_yticks(numpy.arange(0, 1.1, 0.1))

xpos = numpy.arange(len(xorder))
bulk_hets = []
for sname in xorder:
    bulk_hets.append(bulk_estimates.loc[bulk_estimates['sci-lite_sample'] == sname, 'LHON_het'].to_numpy()[0])
axes.plot(xpos, bulk_hets, color='k', marker='*', linestyle='', zorder=100, label='bulk het estimate')
axes.legend(bbox_to_anchor=[1,1])

fig.tight_layout()

fig, axes = pyplot.subplots(nrows=1, ncols=1, figsize=(10,4))
xorder = ['d0_lhon_r1', 'd0_lhon_r2', 'd0_lhon_r3',
          'd5_low_lhon_r1', 'd5_low_lhon_r3',
          'd5_med_lhon_r1', 'd5_med_lhon_r2', 'd5_med_lhon_r3',
          'd5_high_lhon_r1', 'd5_high_lhon_r2', 'd5_high_lhon_r3',
          'd5_unsort_lhon_r1', 'd5_unsort_lhon_r2', 'd5_unsort_lhon_r3',
          'd0_silent_r1', 'd0_silent_r2', 'd0_silent_r3',
          'd5_low_silent_r1', 'd5_low_silent_r2', 'd5_low_silent_r3',
          'd5_high_silent_r1', 'd5_high_silent_r2', 'd5_high_silent_r3',
          'd5_unsort_silent_r1', 'd5_unsort_silent_r2', 'd5_unsort_silent_r3']
seaborn.boxplot(data=valid_cells, x='condition', y='SILENT_ONLY_het', ax=axes, order=xorder, showfliers=False)
seaborn.stripplot(data=valid_cells, x='condition', y='SILENT_ONLY_het', dodge=True, s=1, order=xorder, ax=axes)
axes.plot(numpy.arange(len(xorder)), valid_cells.groupby('condition')['SILENT_ONLY_het'].mean()[xorder],
          color='k', marker='^', linestyle='', zorder=100, label='mean het estimate')

for patch in axes.artists:
    r, g, b, a = patch.get_facecolor()
    patch.set_facecolor((r, g, b, 0))
    patch.set_edgecolor((r, g, b, 1))
    
axes.set_xticklabels(axes.get_xticklabels(), ha='right', rotation=45)
axes.set_ylim((-0.03, 1.03))
axes.set_yticks(numpy.arange(0, 1.1, 0.1))

xpos = numpy.arange(len(xorder))
bulk_hets = []
for sname in xorder:
    bulk_hets.append(bulk_estimates.loc[bulk_estimates['sci-lite_sample'] == sname, 'SILENT_het'].to_numpy()[0])
axes.plot(xpos, bulk_hets, color='k', marker='*', linestyle='', zorder=100, label='bulk het estimate')
axes.legend(bbox_to_anchor=[1,1])

fig.tight_layout()

valid_cells['condition_no_rep'] = valid_cells['condition'].str.split('_r', expand=True)[0]

fig, axes = pyplot.subplots(nrows=1, ncols=1, figsize=(10,4))
xorder = ['d0_lhon', 'd5_low_lhon', 'd5_med_lhon', 'd5_high_lhon', 'd5_unsort_lhon',
          'd0_silent', 'd5_low_silent', 'd5_high_silent', 'd5_unsort_silent']
seaborn.boxplot(data=valid_cells, x='condition_no_rep', y='LHON_het', ax=axes, order=xorder, showfliers=False)
seaborn.stripplot(data=valid_cells, x='condition_no_rep', y='LHON_het', dodge=True, s=1, order=xorder, ax=axes)
axes.plot(numpy.arange(len(xorder)), valid_cells.groupby('condition_no_rep')['LHON_het'].mean()[xorder],
          color='k', marker='^', linestyle='', zorder=100, label='mean het estimate')

xpos = numpy.arange(len(xorder))
bulk_hets = []
for sname in xorder:
    sidx = bulk_estimates['sci-lite_sample'].str.startswith(sname)
    bulk_hets.append(bulk_estimates.loc[sidx, ['LHON', 'LHON_BY1', 'LHON_BY2', 
                                               'LHON_BY3', 'LHON_BY4']].to_numpy().sum()
                     / bulk_estimates.loc[sidx, 'TOTAL'].to_numpy().sum())
axes.plot(xpos, bulk_hets, color='k', marker='*', linestyle='', zorder=100, label='bulk het estimate')

for patch in axes.artists:
    r, g, b, a = patch.get_facecolor()
    patch.set_facecolor((r, g, b, 0))
    patch.set_edgecolor((r, g, b, 1))
    
axes.set_xticklabels(axes.get_xticklabels(), ha='right', rotation=45)
axes.set_ylim((-0.03, 1.03))
axes.set_yticks(numpy.arange(0, 1.1, 0.1))
axes.legend(bbox_to_anchor=[1,1])

fig.tight_layout()

fig, axes = pyplot.subplots(nrows=1, ncols=1, figsize=(10,4))
xorder = ['d0_lhon', 'd5_low_lhon', 'd5_med_lhon', 'd5_high_lhon', 'd5_unsort_lhon',
          'd0_silent', 'd5_low_silent', 'd5_high_silent', 'd5_unsort_silent']
seaborn.boxplot(data=valid_cells, x='condition_no_rep', y='SILENT_ONLY_het', ax=axes, order=xorder, showfliers=False)
seaborn.stripplot(data=valid_cells, x='condition_no_rep', y='SILENT_ONLY_het', dodge=True, s=1, order=xorder, ax=axes)
axes.plot(numpy.arange(len(xorder)), valid_cells.groupby('condition_no_rep')['SILENT_ONLY_het'].mean()[xorder],
          color='k', marker='^', linestyle='', zorder=100, label='mean het estimate')

xpos = numpy.arange(len(xorder))
bulk_hets = []
for sname in xorder:
    sidx = bulk_estimates['sci-lite_sample'].str.startswith(sname)
    bulk_hets.append(bulk_estimates.loc[sidx, ['SILENT', 'SILENT_BY1', 'SILENT_BY2']].to_numpy().sum()
                     / bulk_estimates.loc[sidx, 'TOTAL'].to_numpy().sum())
axes.plot(xpos, bulk_hets, color='k', marker='*', linestyle='', zorder=100, label='bulk het estimate')

for patch in axes.artists:
    r, g, b, a = patch.get_facecolor()
    patch.set_facecolor((r, g, b, 0))
    patch.set_edgecolor((r, g, b, 1))
    
axes.set_xticklabels(axes.get_xticklabels(), ha='right', rotation=45)
axes.set_ylim((-0.03, 1.03))
axes.set_yticks(numpy.arange(0, 1.1, 0.1))
axes.legend(bbox_to_anchor=[1,1])

fig.tight_layout()

fig, axes = pyplot.subplots(nrows=3, ncols=3, figsize=(12,12), sharey=True, sharex=True)
xorder = ['d0_lhon', 'd5_low_lhon', 'd5_med_lhon', 'd5_high_lhon', 'd5_unsort_lhon',
          'd0_silent', 'd5_low_silent', 'd5_high_silent', 'd5_unsort_silent']
for idx, cond in enumerate(xorder):
    ax_idx = (idx//3, idx%3)
    seaborn.scatterplot(data=valid_cells.loc[valid_cells['condition_no_rep'] == cond], 
                        x='LHON_het', y='SILENT_ONLY_het', color=f'C{idx}', s=5, ax=axes[ax_idx])
    axes[ax_idx].set_xlim((-0.05, 1.05))
    axes[ax_idx].set_ylim((-0.05, 1.05))
    axes[ax_idx].set_title(cond)
fig.tight_layout()

fig, axes = pyplot.subplots(nrows=3, ncols=3, figsize=(12,12), sharey=True, sharex=True)
xorder = ['d0_lhon', 'd5_low_lhon', 'd5_med_lhon', 'd5_high_lhon', 'd5_unsort_lhon',
          'd0_silent', 'd5_low_silent', 'd5_high_silent', 'd5_unsort_silent']
for idx, cond in enumerate(xorder):
    ax_idx = (idx//3, idx%3)
    seaborn.kdeplot(data=valid_cells.loc[valid_cells['condition_no_rep'] == cond], 
                    x='LHON_het', y='SILENT_ONLY_het', color=f'C{idx}', ax=axes[ax_idx])
    axes[ax_idx].set_xlim((-0.05, 1.05))
    axes[ax_idx].set_ylim((-0.05, 1.05))
    axes[ax_idx].set_title(cond)
fig.tight_layout()

fig, axes = pyplot.subplots(nrows=3, ncols=3, figsize=(12,12), sharey=True, sharex=True)
xorder = ['d0_lhon', 'd5_low_lhon', 'd5_med_lhon', 'd5_high_lhon', 'd5_unsort_lhon', 
          'd0_silent', 'd5_low_silent', 'd5_high_silent', 'd5_unsort_silent']
for idx, cond in enumerate(xorder):
    ax_idx = (idx//3, idx%3)
    to_plot_subset = valid_cells.loc[valid_cells['condition_no_rep'] == cond]
    seaborn.scatterplot(data=to_plot_subset, x='LHON_het', y='SILENT_ONLY_het', 
                        color=f'C{idx}', s=5, ax=axes[ax_idx])
    xvals = numpy.arange(0,0.8,0.05)
    yvals = -1*xvals + 0.55
    above_line = [(-1*l_het + 0.55) < s_het 
                  for l_het, s_het in to_plot_subset[['LHON_het', 'SILENT_ONLY_het']].to_numpy()]
    pct_above = round((numpy.sum(above_line)/len(above_line))*100, 2)
    axes[ax_idx].plot(xvals, yvals, color='k', linewidth=2)
    axes[ax_idx].set_xlim((-0.05, 1.05))
    axes[ax_idx].set_ylim((-0.05, 1.05))
    axes[ax_idx].set_title(f'{cond}, {pct_above} % above line')
fig.tight_layout()

fig, axes = pyplot.subplots(nrows=3, ncols=3, figsize=(12,12), sharey=True, sharex=True)
xorder = ['d0_lhon', 'd5_low_lhon', 'd5_med_lhon', 'd5_high_lhon', 'd5_unsort_lhon', 
          'd0_silent', 'd5_low_silent', 'd5_high_silent', 'd5_unsort_silent']
for idx, cond in enumerate(xorder):
    ax_idx = (idx//3, idx%3)
    to_plot_subset = valid_cells.loc[valid_cells['condition_no_rep'] == cond]
    seaborn.kdeplot(data=to_plot_subset, x='LHON_het', y='SILENT_ONLY_het', 
                    color=f'C{idx}', ax=axes[ax_idx])
    xvals = numpy.arange(0,0.8,0.05)
    yvals = -1*xvals + 0.55
    above_line = [(-1*l_het + 0.55) < s_het 
                  for l_het, s_het in to_plot_subset[['LHON_het', 'SILENT_ONLY_het']].to_numpy()]
    pct_above = round((numpy.sum(above_line)/len(above_line))*100, 2)
    axes[ax_idx].plot(xvals, yvals, color='k', linewidth=2)
    axes[ax_idx].set_xlim((-0.05, 1.05))
    axes[ax_idx].set_ylim((-0.05, 1.05))
    axes[ax_idx].set_title(f'{cond}, {pct_above} % above line')
fig.tight_layout()

fig, axes = pyplot.subplots(nrows=3, ncols=3, figsize=(12,12), sharey=True, sharex=True)
xorder = ['d0_lhon', 'd5_low_lhon', 'd5_med_lhon', 'd5_high_lhon', 'd5_unsort_lhon', 
          'd0_silent', 'd5_low_silent', 'd5_high_silent', 'd5_unsort_silent']
for idx, cond in enumerate(xorder):
    ax_idx = (idx//3, idx%3)
    to_plot_subset = valid_cells.loc[valid_cells['condition_no_rep'] == cond]
    seaborn.scatterplot(data=to_plot_subset, x='LHON_het', y='SILENT_ONLY_het', 
                        color=f'C{idx}', s=5, ax=axes[ax_idx])
    xvals = [0,0.2,0.2]
    yvals = [0.2, 0.2, 0]
    above_line = [(s_het > 0.2) or (l_het > 0.2) 
                  for l_het, s_het in to_plot_subset[['LHON_het', 'SILENT_ONLY_het']].to_numpy()]
    pct_above = round((numpy.sum(above_line)/len(above_line))*100, 2)
    axes[ax_idx].plot(xvals, yvals, color='k', linewidth=2)
    axes[ax_idx].set_xlim((-0.05, 1.05))
    axes[ax_idx].set_ylim((-0.05, 1.05))
    axes[ax_idx].set_title(f'{cond}, {pct_above} % above line')
fig.tight_layout()

fig, axes = pyplot.subplots(nrows=3, ncols=3, figsize=(12,12), sharey=True, sharex=True)
xorder = ['d0_lhon', 'd5_low_lhon', 'd5_med_lhon', 'd5_high_lhon', 'd5_unsort_lhon', 
          'd0_silent', 'd5_low_silent', 'd5_high_silent', 'd5_unsort_silent']
for idx, cond in enumerate(xorder):
    ax_idx = (idx//3, idx%3)
    to_plot_subset = valid_cells.loc[valid_cells['condition_no_rep'] == cond]
    seaborn.kdeplot(data=to_plot_subset, x='LHON_het', y='SILENT_ONLY_het', 
                    color=f'C{idx}', ax=axes[ax_idx])
    xvals = [0,0.2,0.2]
    yvals = [0.2, 0.2, 0]
    above_line = [(s_het > 0.2) or (l_het > 0.2) 
                  for l_het, s_het in to_plot_subset[['LHON_het', 'SILENT_ONLY_het']].to_numpy()]
    pct_above = round((numpy.sum(above_line)/len(above_line))*100, 2)
    axes[ax_idx].plot(xvals, yvals, color='k', linewidth=2)
    axes[ax_idx].set_xlim((-0.05, 1.05))
    axes[ax_idx].set_ylim((-0.05, 1.05))
    axes[ax_idx].set_title(f'{cond}, {pct_above} % above line')
fig.tight_layout()